Whole-Genome Shotgun Sequencing for Pollen Identification
Anya Cutler and Berry Brosi
started 16 August 2018; most recent edits 27 November 2018
1 overview
The basic idea behind this analysis is to largely repeat the analysis we did on the mixed-amplicon paper, especially given that the inputs (known / constructed pollen mixtures) are exactly the same; this .Rmd file is based directly on that analysis.
The pollen input samples were constructed to vary in three dimensions:
- Question 1: species richness (1-9 species)
- Question 2: rarity (actual proportion of grains this taxon has in a sample; from roughly half & half to < 1% of the rarer type)
- Question 3: taxonomic relatedness (within genus to across broad clades in the seed plants)
The mixed-amplicon paper included two broad classes of analyses:
- Qualitative matching: presence vs. absence of species in samples
- false negative analysis (binomial GLMMs, response = taxon presence in a sample)
- Quantitative matching: proportion of reads in sample relative to proportion of pollen grains in mixture
In addition to repeating qualitative and quantitative analysis of the true positives/false negatives for the empirical and simulation WGS data, we will additionally do the following:
- WGS vs. Amplicon data
- while it would be uninformative to assess presence / absence of false positive (since they are essentially ubiquitous in both the WGS and the amplicon-based data), we can actually analyze either counts or proportions (maybe proportions better? i.e. with binomial errors) of how many reads were false positives (vs. true positives) were in each sample
- this should be quite straightforward, i.e. binomial-errors mixed effects model with sample ID nested within sample type as random effects (here I think we do not need to include ‘species’ as a random effect becuase the species mixtures are effectively covered by the sample type; and for each sample type we should have at least one sample from the amplicon data and from the WGS data—essentially by setting up the random effects in this way we are running a paired-samples test); and the only fixed effect being the analysis method (amplicon vs. Kraken)
- The more time-consuming bit will be to aggregate the counts—for each sample—of how many of the reads were true positives vs. false positives, merged into a single file for both the amplicon data and the Kraken data, with a column added for
sample.sourceetc. One key part of doing this will be to exclude samples from the amplicon data which do not have a corresponding Kraken data point—we want all samples to be “paired”. - when we go to do the
dada2vs.QIIMEanalysis, this false-positive analysis will be particularly key.
- A comparison of the Kraken simulation vs. empirical results (probably qualitative? but we will have to analyze the simulations in the same way as the empirical data)
2 data import
four data sets to import: 1) WGS Kraken empirical data; 2) Kraken simulation results; 3) sample metadata; 4) amplicon data
krak = read.csv("kraken.csv")
mixes = read.csv("pollen-mixes-proportions.csv")
simkrak = read.csv("kraken_sim.csv")
amp_its_family = read.csv("Amplicon_ITS_Family.csv")
amp_its_genus = read.csv("Amplicon_ITS_Genus.csv")
amp_its_species = read.csv("Amplicon_ITS_Species.csv")
amp_rbc_family = read.csv("Amplicon_rbcL_Family.csv")
amp_rbc_genus = read.csv("Amplicon_rbcL_Genus.csv")
amp_rbc_species = read.csv("Amplicon_rbcL_Species.csv")
amp_all = read.csv("Amplicon_all.csv")
# 'mixes' comes in with some rows duplicated (because in the spreadsheet, each was assessed with both ITS2 and with rbcL); fix this here:
mixes = unique(mixes)
# 'krak' and 'simkrak' come with an extra numeric column at the beginning ('X'); delete
krak = krak[,-1]
simkrak = simkrak[,-1]
# change name of 'krak' & 'simkrak' identifier columns
# identifier column is called 'mix.id' but it is very different from the 'mix.ID' column in the 'mixes' data; we will ultimately want to join by 'mix.ID'
names(krak)[1] = "sample.id"
names(simkrak)[1] = "sample.id"
# check them out to make sure we're all good:
# View(krak)
# View(simkrak)
# View(mixes)3 data formatting / setup
- C.1 Kraken data prep
- Jamie formatted these (25-Apr-2018) from the raw Kraken output to have the family / genus / species separated out into columns; see
Shotgun_data_prep.Rmd - unlike the QIIME Illumina data, we do not need to do any aggregation of read counts as this was done automatically by Kraken (nice touch)
- step 1: filter to include only matches at family / genus / species taxonomic resolutions (not anything coarser, and no intermediate clades)
- step 2: create new columns for
mix.IDthat matches the column in the sample (pollen-mixes-proportions.csv) data
- Jamie formatted these (25-Apr-2018) from the raw Kraken output to have the family / genus / species separated out into columns; see
- C.2 Combine Kraken and sample data
- C.3 Remove reads below contamination thresshold
- C.4 Match Kraken and sample data, qualitatively and quantitatively
- C.5 Format Kraken data for false positive analysis
- C.6 Combine simulation and empirical Kraken data
- C.7 Combine amplicon and empirical Kraken data
3.1 Kraken data prep:
- filter by
tax.catcolumn, including only species / genus / family levels - create new column for
mix.IDthat matches the column in the sample (pollen-mixes-proportions.csv) data
# C.1.1: Kraken filtering (easy-peasy)
# first, filter out the reads to only include those at the family / genus / species levels
krak = filter(krak, tax == "F" | tax == "G" | tax == "S")
# for simkrak, results are already only order / family / genus / species matches
# thus, just exclude order-level matches
simkrak = filter(simkrak, tax != "O")
# C.1.2: create new column for `mix.ID` that matches the column in the sample (`pollen-mixes-proportions.csv`) data
# this is a bit more involved... a primary issue is that Emory and UGA used different naming conventions for the samples
# also create a new 'replicate.ID'; we will use this later to back-fill in the 'mixes' dataframe
## MIX.ID
## FIRST ONLY FOR 'KRAK' ('SIMKRAK' comes later)
# first, create the column; fill in temporarily the first 6 characters in the 'sample.id' column
krak$mix.ID = substr(krak$sample.id, 1, 6)
# move the new column to be second in order (not at the end where it's hard to see)
krak = krak[, c(1, ncol(krak), 3:ncol(krak)-1)]
#Want to remove the 6th character if mix.id does not contain the word "mix" or contains 2 dashes. Only want six characters if the mix number is double digits.
krak$mix.ID = ifelse(!grepl("mix", krak$mix.ID) | str_count(krak$mix.ID, pattern="-")==2, substr(krak$mix.ID, 1, 5), krak$mix.ID)
# second, replace underscores and dashes in the temp 'mix.ID' with periods so that they match the 'mixes' data
krak$mix.ID = gsub("-", ".", krak$mix.ID)
krak$mix.ID = gsub("_", ".", krak$mix.ID)
# 2.1--fix capitalization for pecan ("c.ill"" should be "C.ill")
krak$mix.ID = gsub("c.i", "C.i", krak$mix.ID)
# third, for the Emory mixes (different label), extract the mix from the text in the 'sample.id' column
# use 'str_extract' from the 'stringr' library (thank you tidyverse!) plus 'replace'
# (... took me quite a while to figure this out, dang regular expressions...)
# then do some cleanup
# 4.1--extract strings and subset to only the relevant values
new.vals = str_extract(krak$sample.id, "mix_.*/")
new.vals = new.vals[is.na(new.vals) == F]
# 4.2--replace old vals with new.vals
krak$mix.ID = replace(krak$mix.ID, krak$mix.ID=="repla", new.vals)
# 4.3--cleanup
krak$mix.ID = gsub("_", ".", krak$mix.ID)
krak$mix.ID = gsub("/", "", krak$mix.ID)
# fourth, remove periods in between the text "mix" and the number
krak$mix.ID = gsub("mix.", "mix", krak$mix.ID)
## 'REP.ID'
# noticed a weird quirk of the data: in the "B.pap" samples, the "1" at the end of the 'sample.id' is cut off. It may not really matter, but by making it consistent it will help make there be fewer levels / potential complications when appending onto the 'mixes' data
krak$sample.id = as.character(krak$sample.id)
indexy = str_sub(krak$sample.id, -1, -1)==0 # identifies which are missing the '1' at the end
# indexy = which(krak$mix.ID=="B.pap" & krak$rep.ID=="01.R")
krak$sample.id = replace(krak$sample.id, indexy, paste(krak$sample.id[indexy],"1", sep = ""))
# first, create the column; fill in the relevant characters from the 'sample.id' column
krak$rep.ID = substr(krak$sample.id, nchar(as.character(krak$sample.id))-7, nchar(as.character(krak$sample.id))-4) #
# switch underscore to period
krak$rep.ID = gsub("_", ".", krak$rep.ID)
# move the new column to be second in order (not at the end where it's hard to see)
krak = krak[, c(1:2, ncol(krak), 4:ncol(krak)-1)]
## Sample number
sample_sep <- colsplit(krak$sample.id, "_", c("ID", "sample", "L", "R", "Num"))
krak$sample <- ifelse(startsWith(sample_sep$L,"S"), sample_sep$L, ifelse(startsWith(sample_sep$L,"1") | startsWith(sample_sep$L,"2"), sample_sep$R, sample_sep$sample))
##Create list of unique combinations of mix ID, sample number, and rep ID to properly merge kraken with mixes data at later point
krak_uniqueid <- data.frame(krak$mix.ID, krak$rep.ID, krak$sample)
krak_uniqueid <- unique.data.frame(krak_uniqueid)
# NOW FOR SIMKRAK
#Create a column for mix.id with the characters 6-10 of the sample id
simkrak$mix.ID = substr(simkrak$sample.id, 6, 10)
#Remove underscore for mixes with single digit
simkrak$mix.ID = gsub("_", "", simkrak$mix.ID)
#Move to second column as done in krak
simkrak = simkrak[, c(1, ncol(simkrak), 3:ncol(simkrak)-1)]
#Create a column for sequence depth with the last 3 digits of the sample id
simkrak$depth = substr(simkrak$sample.id, (str_length(simkrak$sample.id)-2), str_length(simkrak$sample.id))
#Move to third column
simkrak = simkrak[, c(1:2, ncol(simkrak), 4:ncol(simkrak)-1)]3.2 combine Kraken & sample / mix data:
new aggregated datasheeet, based on sample metadata, but which matches sample data back to the Kraken data so we can run analyses about probability of matching (both qualitative and quantitative). In particular this is taking account of sample “replicates” (not true replicates at all, but forward vs. reverse reads); also different Illumina lanes in the Emory Genome Center data.
- Merge the unique krak IDs with mixes to subset the sample data to only the samples in the kraken dataset
- Create a dataset for analysis of false negatives by only including taxa that are present in sample data
- Create a dataset for analysis of false positives by including all taxa in kraken data
use ‘mix.ID’ as the variable to combine by…
## Merge sample and replicate IDs with mixes data
krak_mixes <- merge(mixes,krak_uniqueid, by.x="mix.ID", by.y="krak.mix.ID")
#Create separate datasets for family, genus, and species level, both including and excluding false positives
truepos.krak.family = merge(krak_mixes, filter(krak, tax == "F"), by = c("mix.ID", "family"), all.x=T)
truepos.krak.genus = merge(krak_mixes, filter(krak, tax == "G"), by = c("mix.ID", "genus"), all.x=T)
truepos.krak.species = merge(krak_mixes, filter(krak, tax == "S"), by = c("mix.ID", "species"), all.x=T)
all.krak.family = merge(krak_mixes, filter(krak, tax == "F"), by = c("mix.ID", "family"), all.x = T, all.y = T)
all.krak.genus = merge(krak_mixes, filter(krak, tax == "G"), by = c("mix.ID", "genus"), all.x = T, all.y = T)
all.krak.species = merge(krak_mixes, filter(krak, tax == "S"), by = c("mix.ID", "species"), all.x = T, all.y = T)
#Repeat for SIMKRAK - Because there are no reps, need to subset mixes to the mixIDs contained in simkrak and merge data
sim_mixes <- subset(mixes, mixes$mix.ID %in% simkrak$mix.ID)
truepos.simkrak.family = merge(sim_mixes, filter(simkrak, tax == "F"), by = c("mix.ID", "family"), all.x = T)
truepos.simkrak.genus = merge(sim_mixes, filter(simkrak, tax == "G"), by = c("mix.ID", "genus"), all.x = T)
truepos.simkrak.species = merge(sim_mixes, filter(simkrak, tax == "S"), by = c("mix.ID", "species"), all.x = T)
all.simkrak.family = merge(sim_mixes, filter(simkrak, tax == "F"), by = c("mix.ID", "family"), all.x = T, all.y = T)
all.simkrak.genus = merge(sim_mixes, filter(simkrak, tax == "G"), by = c("mix.ID", "genus"), all.x = T, all.y=T)
all.simkrak.species = merge(sim_mixes, filter(simkrak, tax == "S"), by = c("mix.ID", "species"), all.x = T, all.y=T)3.3 remove reads below the isolation/PCR negative control
- remove reads below the isolation / PCR negative control thresholds
- this step is only for the empirical data (unnecessary for the simulated data)
- ideally, would base these on negative controls for a given Illumina run (two separate runs here, at UGA and Emory Genome center; we will need to keep those separate); but unfortunately there do not appear to be negative controls for the Emory Genome Center data; I will use the UGA thresholds for all the data
- this entails removing rows with k-mer counts below the threshold
- in the Illumina QIIME amplicon data, we did this separately for (entire) taxa below the threshold level, and for reads, because of the way that the data were formatted in a taxon-by-sample matrix; the Kraken output is already formatted in a way that is closer to
tidyso we only need to do this step once here.
- remove negative control rows (once the above is completed)
#Establish thresshold for maximum number of reads in the negative controls
maxy = max(krak$hits[krak$mix.ID=="negat"])
#Identify rows that fall below this thresshold for false negative analysis
truepos.indexy_family = which(truepos.krak.family$hits <= maxy)
truepos.indexy_genus = which(truepos.krak.genus$hits <= maxy)
truepos.indexy_species = which(truepos.krak.species$hits <= maxy)
#No rows for false negative analysis with read level below thresshold - can continue with analysis
#Identify rows that fall below thresshold for false positive analysis
all.indexy_family = which(all.krak.family$hits <= maxy)
all.indexy_genus = which(all.krak.genus$hits <= maxy)
all.indexy_species = which(all.krak.species$hits <= maxy)
#Remove these rows
all.krak.family = all.krak.family[-all.indexy_family,]
all.krak.genus = all.krak.genus[-all.indexy_genus,]
all.krak.species = all.krak.species[-all.indexy_species,]
#Remove negative control rows
all.krak.family = filter(all.krak.family, mix.ID!="negat")
all.krak.genus = filter(all.krak.genus, mix.ID!="negat")
all.krak.species = filter(all.krak.species, mix.ID!="negat")3.4 match Kraken data to sample data, quantitatively and qualitatively
- Convert the “percentage hits” to a proportion - which will be the response variable in the quantitative models.
- Create a qualitative variable that equals 1 if the proportion is greater than 0, and 0 if else.
#Create quantitative variable by simply dividing the percentage hits (perc.hit) by 100
truepos.krak.family$quant.family = truepos.krak.family$perc.hit/100
truepos.krak.genus$quant.genus = truepos.krak.genus$perc.hit/100
truepos.krak.species$quant.species = truepos.krak.species$perc.hit/100
#For taxa that were not detected, NAs are currently present. Need to change this to 0 for quantitative variable before running analysis.
truepos.krak.family$quant.family = ifelse(is.na(truepos.krak.family$quant.family), 0, truepos.krak.family$quant.family)
truepos.krak.genus$quant.genus = ifelse(is.na(truepos.krak.genus$quant.genus), 0, truepos.krak.genus$quant.genus)
truepos.krak.species$quant.species = ifelse(is.na(truepos.krak.species$quant.species), 0, truepos.krak.species$quant.species)
#Create qualitative variable based on quantitative variable
truepos.krak.family$qual.family = ifelse(truepos.krak.family$quant.family > 0, 1, 0)
truepos.krak.genus$qual.genus = ifelse(truepos.krak.genus$quant.genus > 0, 1, 0)
truepos.krak.species$qual.species = ifelse(truepos.krak.species$quant.species > 0, 1, 0)
##Repeat for SIMKRAK
truepos.simkrak.family$quant.family = truepos.simkrak.family$perc.hit/100
truepos.simkrak.genus$quant.genus = truepos.simkrak.genus$perc.hit/100
truepos.simkrak.species$quant.species = truepos.simkrak.species$perc.hit/100
truepos.simkrak.family$quant.family = ifelse(is.na(truepos.simkrak.family$quant.family), 0, truepos.simkrak.family$quant.family)
truepos.simkrak.genus$quant.genus = ifelse(is.na(truepos.simkrak.genus$quant.genus), 0, truepos.simkrak.genus$quant.genus)
truepos.simkrak.species$quant.species = ifelse(is.na(truepos.simkrak.species$quant.species), 0, truepos.simkrak.species$quant.species)
truepos.simkrak.family$qual.family = ifelse(truepos.simkrak.family$quant.family > 0, 1, 0)
truepos.simkrak.genus$qual.genus = ifelse(truepos.simkrak.genus$quant.genus > 0, 1, 0)
truepos.simkrak.species$qual.species = ifelse(truepos.simkrak.species$quant.species > 0, 1, 0)3.5 format kraken data for false positive analysis
To compare kraken data with the amplicon data, need to have aggregated counts of the “true positive” and “false positive” reads by sample. These will be our “success” and “failure” numbers in a binomial mixed model.
- If the merged sample data is N/A, this indicates the taxa is a false positive. Based on N/A values, create a variable indicating if taxa is true or false positive.
- Remove taxa that are in sample data but not kraken data (false negatives) - not considered for this analysis
- Add “K” to the sample names to distinguish from the samples in the amplicon data (formatted in C.7)
- Aggregate the counts per sample ID by “true positive” and “false positive”
#For false positive analysis, create variable indicating if taxa if "false positive" or "true positive"
all.krak.family$type <- ifelse(is.na(all.krak.family$question.1), "false_pos", "true_pos")
all.krak.genus$type <- ifelse(is.na(all.krak.genus$question.1), "false_pos", "true_pos")
all.krak.species$type <- ifelse(is.na(all.krak.species$question.1), "false_pos", "true_pos")
#If hits = N/A, indicates a false negative (in sample data but not in Kraken data). Remove because we are not considering for this analysis.
all.krak.family <- all.krak.family[-which(is.na(all.krak.family$sample.id)),]
all.krak.genus <- all.krak.genus[-which(is.na(all.krak.genus$sample.id)),]
all.krak.species <- all.krak.species[-which(is.na(all.krak.species$sample.id)),]
#Add "K_" to sample name
all.krak.family$sample <- paste("K_",all.krak.family$sample, sep="")
all.krak.genus$sample <- paste("K_",all.krak.genus$sample, sep="")
all.krak.species$sample <- paste("K_",all.krak.species$sample, sep="")
#Aggregate counts by mix.ID, sample.ID, and type
agg.krak.family <- all.krak.family %>%
select(mix.ID, family, rep.ID, sample, type, hits) %>%
group_by(mix.ID, sample, rep.ID, type) %>%
summarize(total_hits = sum(hits))
agg.krak.genus <- all.krak.genus %>%
select(mix.ID, genus, rep.ID, sample, type, hits) %>%
group_by(mix.ID, sample, rep.ID, type) %>%
summarize(total_hits = sum(hits))
agg.krak.species <- all.krak.species %>%
select(mix.ID, species, rep.ID, sample, type, hits) %>%
group_by(mix.ID, sample, rep.ID, type) %>%
summarize(total_hits = sum(hits))
#Convert to wide format with one column for true positive hits and one column for false positive hits
agg.krak.family <- spread(agg.krak.family, key=type, value=total_hits)
agg.krak.genus <- spread(agg.krak.genus, key=type, value=total_hits)
agg.krak.species <- spread(agg.krak.species, key=type, value=total_hits)
#If positives are N/A, set to equal 0
agg.krak.family$true_pos = ifelse(is.na(agg.krak.family$true_pos),0, agg.krak.family$true_pos)
agg.krak.genus$true_pos = ifelse(is.na(agg.krak.genus$true_pos),0, agg.krak.genus$true_pos)
agg.krak.species$true_pos = ifelse(is.na(agg.krak.species$true_pos),0, agg.krak.species$true_pos)
agg.krak.family$false_pos = ifelse(is.na(agg.krak.family$false_pos),0, agg.krak.family$false_pos)
agg.krak.genus$false_pos = ifelse(is.na(agg.krak.genus$false_pos),0, agg.krak.genus$false_pos)
agg.krak.species$false_pos = ifelse(is.na(agg.krak.species$false_pos),0, agg.krak.species$false_pos)
#Repeat for simkrak
all.simkrak.family$type <- ifelse(is.na(all.simkrak.family$question.1), "false_pos", "true_pos")
all.simkrak.genus$type <- ifelse(is.na(all.simkrak.genus$question.1), "false_pos", "true_pos")
all.simkrak.species$type <- ifelse(is.na(all.simkrak.species$question.1), "false_pos", "true_pos")
all.simkrak.family$hits <- ifelse(is.na(all.simkrak.family$hits),0,all.simkrak.family$hits)
all.simkrak.genus$hits <- ifelse(is.na(all.simkrak.genus$hits),0,all.simkrak.genus$hits)
all.simkrak.species$hits <- ifelse(is.na(all.simkrak.species$hits),0,all.simkrak.species$hits)
agg.simkrak.family <- all.simkrak.family %>%
select(mix.ID, depth, family, type, hits) %>%
group_by(mix.ID, depth, type) %>%
summarize(total_hits = sum(hits))
agg.simkrak.genus <- all.simkrak.genus %>%
select(mix.ID, depth, genus, type, hits) %>%
group_by(mix.ID, depth, type) %>%
summarize(total_hits = sum(hits))
agg.simkrak.species <- all.simkrak.species %>%
select(mix.ID, depth, species, type, hits) %>%
group_by(mix.ID, depth, type) %>%
summarize(total_hits = sum(hits))
agg.simkrak.family <- spread(agg.simkrak.family, key=type, value=total_hits)
agg.simkrak.genus <- spread(agg.simkrak.genus, key=type, value=total_hits)
agg.simkrak.species <- spread(agg.simkrak.species, key=type, value=total_hits)
agg.simkrak.family$true_pos = ifelse(is.na(agg.simkrak.family$true_pos),0, agg.simkrak.family$true_pos)
agg.simkrak.genus$true_pos = ifelse(is.na(agg.simkrak.genus$true_pos),0, agg.simkrak.genus$true_pos)
agg.simkrak.species$true_pos = ifelse(is.na(agg.simkrak.species$true_pos),0, agg.simkrak.species$true_pos)
agg.simkrak.family$false_pos = ifelse(is.na(agg.simkrak.family$false_pos),0, agg.simkrak.family$false_pos)
agg.simkrak.genus$false_pos = ifelse(is.na(agg.simkrak.genus$false_pos),0, agg.simkrak.genus$false_pos)
agg.simkrak.species$false_pos = ifelse(is.na(agg.simkrak.species$false_pos),0, agg.simkrak.species$false_pos)3.6 combine simulation and Kraken data
- Create a variable indicating if data is simulated or empirical
- rbind mix.ID, true_pos, and false_pos
- Also create subsetted datasets that only including sampling depth of 1GB or 2GB to test separately
#Create "source" variable
agg.krak.family$source = "krak"
agg.krak.genus$source = "krak"
agg.krak.species$source = "krak"
agg.simkrak.family$source = "sim"
agg.simkrak.genus$source = "sim"
agg.simkrak.species$source = "sim"
#Create a sample and rep.ID variable for simkrak data, set all to 1
agg.simkrak.family$sample = 1
agg.simkrak.genus$sample = 1
agg.simkrak.species$sample = 1
agg.simkrak.family$rep.ID = 1
agg.simkrak.genus$rep.ID = 1
agg.simkrak.species$rep.ID = 1
#Identify which rows are 1GB depth and which rows are 2GB depth
simkrak_family_1GB = which(agg.simkrak.family$depth == "1GB")
simkrak_family_2GB = which(agg.simkrak.family$depth == "2GB")
simkrak_genus_1GB = which(agg.simkrak.genus$depth == "1GB")
simkrak_genus_2GB = which(agg.simkrak.genus$depth == "2GB")
simkrak_species_1GB = which(agg.simkrak.species$depth == "1GB")
simkrak_species_2GB = which(agg.simkrak.species$depth == "2GB")
krak_simkrak_agg_family_all = (rbind(as.data.frame(agg.krak.family), agg.simkrak.family[,c(1,3:7)]))
krak_simkrak_agg_genus_all = (rbind(as.data.frame(agg.krak.genus), agg.simkrak.genus[,c(1,3:7)]))
krak_simkrak_agg_species_all = (rbind(as.data.frame(agg.krak.species), agg.simkrak.species[,c(1,3:7)]))
krak_simkrak_agg_family_1GB = (rbind(as.data.frame(agg.krak.family), agg.simkrak.family[simkrak_family_1GB,c(1,3:7)]))
krak_simkrak_agg_genus_1GB = (rbind(as.data.frame(agg.krak.genus), agg.simkrak.genus[simkrak_genus_1GB,c(1,3:7)]))
krak_simkrak_agg_species_1GB = (rbind(as.data.frame(agg.krak.species), agg.simkrak.species[simkrak_species_1GB,c(1,3:7)]))
krak_simkrak_agg_family_2GB = (rbind(as.data.frame(agg.krak.family), agg.simkrak.family[simkrak_family_2GB,c(1,3:7)]))
krak_simkrak_agg_genus_2GB = (rbind(as.data.frame(agg.krak.genus), agg.simkrak.genus[simkrak_genus_2GB,c(1,3:7)]))
krak_simkrak_agg_species_2GB = (rbind(as.data.frame(agg.krak.species), agg.simkrak.species[simkrak_species_2GB,c(1,3:7)]))3.7 combine WGS and amplicon data for false positive analysis
We need to combine the WGS with amplicon data by creating a dataframe showing the number of true positive and the number of false positive reads by source, mix ID, and sample ID.
#Create list of amplicon data frames
amp_data <- list(amp_its_family, amp_its_genus, amp_its_species, amp_rbc_family, amp_rbc_genus, amp_rbc_species)
#Apply function to list
amp_convert <- lapply(amp_data, function(x){
#convert first column name to "taxa"
names(x)[1] <- "taxa"
#convert from wide to long format
x <- melt(x, id.vars="taxa", value.name="hits")
#separate mix ID and sample ID
x[,4:5] <- colsplit(x$variable, "_", c("mix.ID", "sample"))
#remove variable "id"
x <- x[,-2]
})
#Extract data frames from list
amp_its_family <- as.data.frame(amp_convert[1])
amp_its_genus <- as.data.frame(amp_convert[2])
amp_its_species <- as.data.frame(amp_convert[3])
amp_rbc_family <- as.data.frame(amp_convert[4])
amp_rbc_genus <- as.data.frame(amp_convert[5])
amp_rbc_species <- as.data.frame(amp_convert[6])
#Merge with mixes data by mixID and sampleID
amp_its_family_mix <- merge(amp_its_family, mixes, by.x=c("mix.ID", "taxa"), by.y=c("mix.ID", "family"), all.x=T)
amp_its_genus_mix <- merge(amp_its_genus, mixes, by.x=c("mix.ID", "taxa"), by.y=c("mix.ID", "genus"), all.x=T)
amp_its_species_mix <- merge(amp_its_species, mixes, by.x=c("mix.ID", "taxa"), by.y=c("mix.ID", "species"), all.x=T)
amp_rbc_family_mix <- merge(amp_rbc_family, mixes, by.x=c("mix.ID", "taxa"), by.y=c("mix.ID", "family"), all.x=T)
amp_rbc_genus_mix <- merge(amp_rbc_genus, mixes, by.x=c("mix.ID", "taxa"), by.y=c("mix.ID", "genus"), all.x=T)
amp_rbc_species_mix <- merge(amp_rbc_species, mixes, by.x=c("mix.ID", "taxa"), by.y=c("mix.ID", "species"), all.x=T)
#Rows with mixes variables that are "NA" are false positive taxa. Create a data frame that indicates if row is true positive or false positive
amp_its_family_mix$type <- ifelse(is.na(amp_its_family_mix$question.1), "false_pos", "true_pos")
amp_its_genus_mix$type <- ifelse(is.na(amp_its_genus_mix$question.1), "false_pos", "true_pos")
amp_its_species_mix$type <- ifelse(is.na(amp_its_species_mix$question.1), "false_pos", "true_pos")
amp_rbc_family_mix$type <- ifelse(is.na(amp_rbc_family_mix$question.1), "false_pos", "true_pos")
amp_rbc_genus_mix$type <- ifelse(is.na(amp_rbc_genus_mix$question.1), "false_pos", "true_pos")
amp_rbc_species_mix$type <- ifelse(is.na(amp_rbc_species_mix$question.1), "false_pos", "true_pos")
#New list of mixed datasets
amp_mixed <- list(amp_its_family_mix, amp_its_genus_mix, amp_its_species_mix, amp_rbc_family_mix, amp_rbc_genus_mix, amp_rbc_species_mix)
#Apply function to list
amp_summed <- lapply(amp_mixed, function(x){
#summarize number of hits by mix.ID, sample, and type
x <- x %>%
select(mix.ID, taxa, hits, sample, type) %>%
group_by(mix.ID, sample, type) %>%
summarize(total_hits = sum(as.numeric(hits)))
#Convert to wide format with one column for true positive hits and one column for false positive hits
#x <- reshape(x, idvar="sample", timevar="type", direction="wide")
})
amp_its_family_summ <- as.data.frame(amp_summed[1])
amp_its_genus_summ <- as.data.frame(amp_summed[2])
amp_its_species_summ <- as.data.frame(amp_summed[3])
amp_rbc_family_summ <- as.data.frame(amp_summed[4])
amp_rbc_genus_summ <- as.data.frame(amp_summed[5])
amp_rbc_species_summ <- as.data.frame(amp_summed[6])
#Add "A" to beginning of sample ID - couldn't get this to work in the lapply function
amp_its_family_summ$sample <- paste("A_", amp_its_family_summ$sample, sep="")
amp_its_genus_summ$sample <- paste("A_", amp_its_genus_summ$sample, sep="")
amp_its_species_summ$sample <- paste("A_", amp_its_species_summ$sample, sep="")
amp_rbc_family_summ$sample <- paste("A_", amp_rbc_family_summ$sample, sep="")
amp_rbc_genus_summ$sample <- paste("A_", amp_rbc_genus_summ$sample, sep="")
amp_rbc_species_summ$sample <- paste("A_", amp_rbc_species_summ$sample, sep="")
#Convert to wide format with one column for true positive hits and one column for false positive hits - couldn't get this to work in the lapply function
amp_its_family_reshape <- spread(amp_its_family_summ, key=type, value=total_hits)
amp_its_genus_reshape <- spread(amp_its_genus_summ, key=type, value=total_hits)
amp_its_species_reshape <- spread(amp_its_species_summ, key=type, value=total_hits)
amp_rbc_family_reshape <- spread(amp_rbc_family_summ, key=type, value=total_hits)
amp_rbc_genus_reshape <- spread(amp_rbc_genus_summ, key=type, value=total_hits)
amp_rbc_species_reshape <- spread(amp_rbc_species_summ, key=type, value=total_hits)
#If positives are N/A, set to equal 0
amp_its_family_reshape$true_pos = ifelse(is.na(amp_its_family_reshape$true_pos),0, amp_its_family_reshape$true_pos)
amp_its_genus_reshape$true_pos = ifelse(is.na(amp_its_genus_reshape$true_pos),0, amp_its_genus_reshape$true_pos)
amp_its_species_reshape$true_pos = ifelse(is.na(amp_its_species_reshape$true_pos),0, amp_its_species_reshape$true_pos)
amp_rbc_family_reshape$true_pos = ifelse(is.na(amp_rbc_family_reshape$true_pos),0, amp_rbc_family_reshape$true_pos)
amp_rbc_genus_reshape$true_pos = ifelse(is.na(amp_rbc_genus_reshape$true_pos),0, amp_rbc_genus_reshape$true_pos)
amp_rbc_species_reshape$true_pos = ifelse(is.na(amp_rbc_species_reshape$true_pos),0, amp_rbc_species_reshape$true_pos)
amp_its_family_reshape$false_pos = ifelse(is.na(amp_its_family_reshape$false_pos),0, amp_its_family_reshape$false_pos)
amp_its_genus_reshape$false_pos = ifelse(is.na(amp_its_genus_reshape$false_pos),0, amp_its_genus_reshape$false_pos)
amp_its_species_reshape$false_pos = ifelse(is.na(amp_its_species_reshape$false_pos),0, amp_its_species_reshape$false_pos)
amp_rbc_family_reshape$false_pos = ifelse(is.na(amp_rbc_family_reshape$false_pos),0, amp_rbc_family_reshape$false_pos)
amp_rbc_genus_reshape$false_pos = ifelse(is.na(amp_rbc_genus_reshape$false_pos),0, amp_rbc_genus_reshape$false_pos)
amp_rbc_species_reshape$false_pos = ifelse(is.na(amp_rbc_species_reshape$false_pos),0, amp_rbc_species_reshape$false_pos)
#Add rep.ID = 1 for amplicon data to match kraken data
amp_its_family_reshape$rep.ID = 1
amp_its_genus_reshape$rep.ID = 1
amp_its_species_reshape$rep.ID = 1
amp_rbc_family_reshape$rep.ID = 1
amp_rbc_genus_reshape$rep.ID = 1
amp_rbc_species_reshape$rep.ID = 1
#Move to third column to match Kraken data
amp_its_family_reshape = amp_its_family_reshape[,c(1:2,5,3:4)]
amp_its_genus_reshape = amp_its_genus_reshape[,c(1:2,5,3:4)]
amp_its_species_reshape = amp_its_species_reshape[,c(1:2,5,3:4)]
amp_rbc_family_reshape = amp_rbc_family_reshape[,c(1:2,5,3:4)]
amp_rbc_genus_reshape = amp_rbc_genus_reshape[,c(1:2,5,3:4)]
amp_rbc_species_reshape = amp_rbc_species_reshape[,c(1:2,5,3:4)]
#Add column "source" to amplicon and kraken data
amp_its_family_reshape$source = "amp"
amp_its_genus_reshape$source = "amp"
amp_its_species_reshape$source = "amp"
amp_rbc_family_reshape$source = "amp"
amp_rbc_genus_reshape$source = "amp"
amp_rbc_species_reshape$source = "amp"
agg.krak.family$source = "krak"
agg.krak.genus$source = "krak"
agg.krak.species$source = "krak"
#Merge kraken and amplicon data
krakamp_its_family = rbind(subset(amp_its_family_reshape, mix.ID %in% agg.krak.family$mix.ID), as.data.frame(agg.krak.family))
krakamp_its_genus = rbind(subset(amp_its_genus_reshape, mix.ID %in% agg.krak.genus$mix.ID), as.data.frame(agg.krak.genus))
krakamp_its_species = rbind(subset(amp_its_species_reshape, mix.ID %in% agg.krak.species$mix.ID), as.data.frame(agg.krak.species))
krakamp_rbc_family = rbind(subset(amp_rbc_family_reshape, mix.ID %in% agg.krak.family$mix.ID), as.data.frame(agg.krak.family))
krakamp_rbc_genus = rbind(subset(amp_rbc_genus_reshape, mix.ID %in% agg.krak.genus$mix.ID), as.data.frame(agg.krak.genus))
krakamp_rbc_species = rbind(subset(amp_rbc_species_reshape, mix.ID %in% agg.krak.species$mix.ID), as.data.frame(agg.krak.species))3.8 combine WGS and Kraken data for qualitative true positive / false negative analysis
Using the kraken data formatted for true positive/false negative analysis (“truepos.krak”), rbind with amplicon data.
#Add "K-" to beginning of sample number of kraken dataset and add variable "source" to indicate that data is from kraken dataset
truepos.krak.family$sample <- paste("K-", truepos.krak.family$krak.sample, sep="")
truepos.krak.genus$sample <- paste("K-", truepos.krak.genus$krak.sample, sep="")
truepos.krak.species$sample <- paste("K-", truepos.krak.species$krak.sample, sep="")
#Add "source" variable to indicate if data is WGS or amplicon
truepos.krak.family$source = "krak"
truepos.krak.genus$source = "krak"
truepos.krak.species$source = "krak"
amp_all$source = "amp"
#Concatenate WGS and amplicon data using mapply so that mismatching variable names of the amplicon data will be ignored
krakamp_rbc_family_fn <- as.data.frame(mapply(c, truepos.krak.family[,c("mix.ID", "family", "sample", "qual.family", "source")], amp_all[,c("mix.ID","family","sample", "qual.family.rbcL", "source")]))
krakamp_its_family_fn <- as.data.frame(mapply(c, truepos.krak.family[,c("mix.ID", "family", "sample", "qual.family", "source")], amp_all[,c("mix.ID","family","sample", "qual.family.ITS", "source")]))
krakamp_rbc_genus_fn <- as.data.frame(mapply(c, truepos.krak.genus[,c("mix.ID", "genus", "sample", "qual.genus", "source")], amp_all[,c("mix.ID","genus","sample", "qual.genus.rbcL", "source")]))
krakamp_its_genus_fn <- as.data.frame(mapply(c, truepos.krak.genus[,c("mix.ID", "genus", "sample", "qual.genus", "source")], amp_all[,c("mix.ID","genus","sample", "qual.genus.ITS", "source")]))
krakamp_rbc_species_fn <- as.data.frame(mapply(c, truepos.krak.species[,c("mix.ID", "species", "sample", "qual.species", "source")], amp_all[,c("mix.ID","species","sample", "qual.species.rbcL", "source")]))
krakamp_its_species_fn <- as.data.frame(mapply(c, truepos.krak.species[,c("mix.ID", "species", "sample", "qual.species", "source")], amp_all[,c("mix.ID","species","sample", "qual.species.ITS", "source")]))4 analysis
- 1 Empirical Kraken false negative analysis - qualitative
- 2 Empirical Kraken false negative analysis - quantitative
- 3 Simulation Kraken false negative analysis - qualitative
- 4 Simulation Kraken false negative analysis - quantitative
- 5 Empirical vs. Simulation Kraken data
- 6 Empirical Kraken vs. Amplicon data
- 6.1 AIC comparison of qualitative models
- 6.2 AIC comparison of quantitative models
- 6.3 Binary GLMMs using true positive vs. false positive
- 6.4 Binary GLMMs using qualitative true positives vs. false negatives
4.1 qualitative analysis of empirical false negatives
To assess if species richness, relatedness, and pollen grain proportion have a significant effect on the ability of the empirical WGS to qualitatively detect the presence/absence of a species, use a binomial mixed effects model with species as a random effect and rep.ID nested in sample nested in mix.ID as a random effect.
For consistency, take out Zea and format data tables to have the same name as those in the mixed amplicon analysis.
#Take out Zea
truepos.krak.family = filter(truepos.krak.family, genus.x!= "Zea")
truepos.krak.genus = filter(truepos.krak.genus, genus!="Zea")
truepos.krak.species = filter(truepos.krak.species, genus.x!="Zea")
taxon = c("species", "genus", "family")
# first set up a table for the results:
# I will set this up with 18 entries
results.table = data.frame(question = rep(NA,18), taxon = rep(NA,18), data.subset = rep(NA,18), model.name = rep(NA,18), p.val = rep(1.000001,18), n = rep(9999,18), warning.msg = rep(NA,18))
# keep track of which row of the table to record in:
tracker = 1
# # EXAMPLE FORMULA
# Krak.Q1.species.all = glmer(qual.species.rbcL ~ spp.rich + (1|mix.ID/sample/rep.ID) + (1|species), family = binomial, data = truepos.krak.species, control = glmerControl(optimizer="bobyqa"))
# basis of subsetting:
# sub = filter(data, question.1 ==1 | question.2 ==1 | question.3==1)
datasubset = c("sub", "all") # whether we are using the designated subset of data designed for the question, or all data
# response variables relating to each of the three questions (column names in data)
question = c("spp.rich", "relatedness", "pollen.grain.proportion")
for(q in 1:3) { # 'question': response variables for Q1 / Q2 / Q3
for(k in 1:3){ # 'taxon': species, genus, family
for(l in 1:2) { # 'datasubset': sub or all
# first, name the analysis:
namer = paste("Krak.Q", q, ".", taxon[k], ".", datasubset[l], sep = "")
# # 1.5, print name of which model was evaluated
# print("**************************************************")
# print(paste("********************* ", namer, " *********************"))
# print(paste("question q = ", q, "; taxon k = ", k, "; datasubset l = ", l))
# print("**************************************************")
# second, set which taxonomic data to use:
data.to.use = paste("truepos.krak.", taxon[k], sep = "")
# third, set up the data subset (may or may not use)
subster = paste("data.sub = filter(", data.to.use, ", question.1 == ", q, " | question.2 == ", q, " | question.3 == ",q, ")", sep = "")
eval(parse(text = subster)) # probably not the most efficient thing ever...
# fourth, set whether or not data subset is used (vs. all data)
if(datasubset[l]=="sub") {data.to.use = "data.sub"} # i.e., doesn't change if all data are to be used
# fifth, set up mixed-effects model:
mixed = paste(namer, " = suppressWarnings(glmer(qual.", taxon[k],
" ~ ", question[q], " + (1|mix.ID/krak.sample/krak.rep.ID) + (1|", taxon[k], "), family = binomial,
data = ", data.to.use, ", control = glmerControl(optimizer=\"bobyqa\")))", sep = "")
# sixth, evaluate the mixed-effects model
eval(parse(text = mixed))
# # eighth, print summary of model [SKIP FOR NOW]
# summarizer = paste("print(summary(", namer, "))", sep = "")
# eval(parse(text = summarizer)) # print summary of the mixed-effects model
## extract p-value
# example: coef(summary(Q3.genus.ITS.all))[2,4]
pvaller = paste("pval <- coef(summary(", namer, "))[2,4]", sep = "")
eval(parse(text = pvaller))
# extract convergence failures
converger = paste(namer, "@optinfo$conv$lme4$code", sep = "")
converg = eval(parse(text = converger))
converg.return = ifelse(length(converg)==1, "ERROR!!", "")
# record results in table
results.table[tracker,1] = question[q]
results.table[tracker,2] = taxon[k]
results.table[tracker,3] = datasubset[l]
results.table[tracker,4] = namer
results.table[tracker,5] = pval
results.table[tracker,6] = nrow(eval(parse(text = data.to.use)))
results.table[tracker,7] = converg.return
# advance tracker
tracker = tracker + 1
}
}
}## Warning in vcov.merMod(object, use.hessian = use.hessian): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Warning in vcov.merMod(object, correlation = correlation, sigm = sig): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Warning in vcov.merMod(object, use.hessian = use.hessian): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Warning in vcov.merMod(object, correlation = correlation, sigm = sig): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Warning in vcov.merMod(object, use.hessian = use.hessian): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Warning in vcov.merMod(object, correlation = correlation, sigm = sig): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX# display results table
# note that the 'kable' function is part of the 'knitr' package and `kable_styling` is from the `kableExtra` package
kable(results.table) %>%
kable_styling(bootstrap_options = "striped", full_width = F)| question | taxon | data.subset | model.name | p.val | n | warning.msg |
|---|---|---|---|---|---|---|
| spp.rich | species | sub | Krak.Q1.species.sub | 0.0000000 | 116 | |
| spp.rich | species | all | Krak.Q1.species.all | 0.0003044 | 162 | |
| spp.rich | genus | sub | Krak.Q1.genus.sub | 0.0000015 | 122 | |
| spp.rich | genus | all | Krak.Q1.genus.all | 0.0000000 | 168 | ERROR!! |
| spp.rich | family | sub | Krak.Q1.family.sub | 0.0000000 | 150 | ERROR!! |
| spp.rich | family | all | Krak.Q1.family.all | 0.0000000 | 210 | ERROR!! |
| relatedness | species | sub | Krak.Q2.species.sub | 0.9683069 | 38 | |
| relatedness | species | all | Krak.Q2.species.all | 0.0000000 | 162 | |
| relatedness | genus | sub | Krak.Q2.genus.sub | 0.9682583 | 38 | |
| relatedness | genus | all | Krak.Q2.genus.all | 0.0000015 | 168 | |
| relatedness | family | sub | Krak.Q2.family.sub | 0.0577688 | 50 | |
| relatedness | family | all | Krak.Q2.family.all | 0.0000345 | 210 | |
| pollen.grain.proportion | species | sub | Krak.Q3.species.sub | 0.9999929 | 26 | ERROR!! |
| pollen.grain.proportion | species | all | Krak.Q3.species.all | 0.0000111 | 162 | |
| pollen.grain.proportion | genus | sub | Krak.Q3.genus.sub | 0.9999929 | 26 | ERROR!! |
| pollen.grain.proportion | genus | all | Krak.Q3.genus.all | 0.0000000 | 168 | ERROR!! |
| pollen.grain.proportion | family | sub | Krak.Q3.family.sub | 0.9999956 | 38 | ERROR!! |
| pollen.grain.proportion | family | all | Krak.Q3.family.all | 0.0000000 | 210 |
For the empirical data, species richness has a significant effect on qualitative detection at the species level (in both the subsetted data and full data set) and at the genus level in the subsetted data, but the model gives errors at the genus level using the full dataset and at the family level.
Species relatedness has a significant effect on qualitative detection with the full dataset (highly significant - all p-values <0.0001), but not with the subsetted dataset (p=0.97 for species and genus level, p = 0.06 for family level). Not sure if this makes sense…
Pollen grain proportion has a significant effect on qualitative detection at the species level with the full dataset and at the family level with the full dataset. The other tests gave errors.
4.2 quantitative results of empirical false negatives
To assess if pollen grain proportion is correlated to read proportion in the empirical WGS data, use a linear mixed model with mix.ID and species as a random effect.
# ultimately want 3 analyses: {species, genus, and family}
# species
quant.species = lmer(quant.species ~ pollen.grain.proportion + (1|mix.ID) + (1|species), data = truepos.krak.species)
# genus
quant.genus = lmer(quant.genus ~ pollen.grain.proportion + (1|mix.ID) + (1|genus), data = truepos.krak.genus)
#family
quant.family = lmer(quant.family ~ pollen.grain.proportion + (1|mix.ID) + (1|family), data = truepos.krak.family)
#=========================================
# r-squared calculation at the family level
r2.family = r2beta(quant.family)
# r-squared calculation at the genus level
r2.genus = r2beta(quant.genus)
# r-squared calculation at the species level
r2.species = r2beta(quant.species)
#Merge the slope ("Estimate"), p-value ("Pr...t..") from the mixed models with the r-squared value for each test
coefs_quant.family <- cbind((data.frame(coef(summary(quant.family)))["pollen.grain.proportion",c("Estimate","Pr...t..")]), r2.family[2,6])
coefs_quant.genus <- cbind((data.frame(coef(summary(quant.genus)))["pollen.grain.proportion",c("Estimate","Pr...t..")]), r2.genus[2,6])
coefs_quant.species <- cbind((data.frame(coef(summary(quant.species)))["pollen.grain.proportion",c("Estimate","Pr...t..")]), r2.species[2,6])
#Rename row names and column names
row.names(coefs_quant.family) = "Family"
row.names(coefs_quant.genus) = "Genus"
row.names(coefs_quant.species) = "Species"
colnames(coefs_quant.family) = c("Slope", "p-value", "R2")
colnames(coefs_quant.genus) = c("Slope", "p-value", "R2")
colnames(coefs_quant.species) = c("Slope", "p-value", "R2")
#Merge summary of coefficients into one dataset
coefs_summ <- rbind(coefs_quant.family, coefs_quant.genus, coefs_quant.species)
#Display table of summarized coefficients
kable(coefs_summ) %>%
kable_styling(bootstrap_options = "striped", full_width = F)| Slope | p-value | R2 | |
|---|---|---|---|
| Family | 0.1523498 | 0 | 0.8096692 |
| Genus | 0.0568475 | 0 | 0.5801762 |
| Species | 0.0551695 | 0 | 0.7719810 |
Pollen grain proportion was significantly related to read proportion at the family (R2=0.81, p<0.000001), genus (R2=0.58, p<0.000001), and species level (R2=0.77, p<0.000001)
4.3 qualitative analysis of simulation false negatives
Repeat D1 analysis, but with simulation data.
taxon = c("species", "genus", "family")
# first set up a table for the results:
# I will set this up with 18 entries
simkrak.results.table = data.frame(question = rep(NA,9), taxon = rep(NA,9), model.name = rep(NA,9), p.val = rep(1.000001,9), n = rep(9999,9), warning.msg = rep(NA,9))
# keep track of which row of the table to record in:
tracker = 1
# # EXAMPLE FORMULA
# Krak.Q1.species.all = glmer(qual.species ~ spp.rich + (1|mix.ID) + (1|species), family = binomial, data = truepos.krak.species, control = glmerControl(optimizer="bobyqa"))
# response variables relating to each of the three questions (column names in data)
question = c("spp.rich", "relatedness", "pollen.grain.proportion")
for(q in 1:3) { # 'question': response variables for Q1 / Q2 / Q3
for(k in 1:3){ # 'taxon': species, genus, family
# name the analysis:
namer = paste("Simkrak.Q", q, ".", taxon[k], sep = "")
# set which taxonomic data to use:
data.to.use = paste("truepos.simkrak.", taxon[k], sep = "")
#set up mixed effects model
mixed = paste(namer, " = suppressWarnings(glmer(qual.", taxon[k],
" ~ ", question[q], " + (1|mix.ID) + (1|", taxon[k], "), family = binomial,
data = ", data.to.use, ", control = glmerControl(optimizer=\"bobyqa\")))", sep = "")
# sixth, evaluate the mixed-effects model
eval(parse(text = mixed))
# # eighth, print summary of model [SKIP FOR NOW]
# summarizer = paste("print(summary(", namer, "))", sep = "")
# eval(parse(text = summarizer)) # print summary of the mixed-effects model
## extract p-value
# example: coef(summary(Q3.genus.ITS.all))[2,4]
pvaller = paste("pval <- coef(summary(", namer, "))[2,4]", sep = "")
eval(parse(text = pvaller))
# extract convergence failures
converger = paste(namer, "@optinfo$conv$lme4$code", sep = "")
converg = eval(parse(text = converger))
converg.return = ifelse(length(converg)==1, "ERROR!!", "")
# record results in table
simkrak.results.table[tracker,1] = question[q]
simkrak.results.table[tracker,2] = taxon[k]
simkrak.results.table[tracker,3] = namer
simkrak.results.table[tracker,4] = pval
simkrak.results.table[tracker,5] = nrow(eval(parse(text = data.to.use)))
simkrak.results.table[tracker,6] = converg.return
# advance tracker
tracker = tracker + 1
}
}## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient## Error in coef(summary(Simkrak.Q1.species))[2, 4]: subscript out of bounds# display results table
# note that the 'kable' function is part of the 'knitr' package
# NOT DISPLAYED because of errors:
# kable(simkrak.results.table)Yields an error message that the fixed effects model matrix is rank deficient. The observation number for the simulation data is very small—likely the reason the model won’t run?
4.4 quantitative analysis of simulation false negatives
Repeat D2, but with simulation data.
# ultimately want 3 analyses: {species, genus, and family}
# species
sim.quant.species = lmer(quant.species ~ pollen.grain.proportion + (1|mix.ID) + (1|species), data = truepos.simkrak.species)
# genus
sim.quant.genus = lmer(quant.genus ~ pollen.grain.proportion + (1|mix.ID) + (1|genus), data = truepos.simkrak.genus)
#family
sim.quant.family = lmer(quant.family ~ pollen.grain.proportion + (1|mix.ID) + (1|family), data = truepos.simkrak.family)
#=========================================
# r-squared calculation at the family level
sim.r2.family = r2beta(sim.quant.family)
# r-squared calculation at the genus level
sim.r2.genus = r2beta(sim.quant.genus)
# r-squared calculation at the species level
sim.r2.species = r2beta(sim.quant.species)
#Merge the slope ("Estimate"), p-value ("Pr...t..") from the mixed models with the r-squared value for each test
sim.coefs_quant.family <- cbind((data.frame(coef(summary(sim.quant.family)))["pollen.grain.proportion",c("Estimate","Pr...t..")]), sim.r2.family[2,6])
sim.coefs_quant.genus <- cbind((data.frame(coef(summary(sim.quant.genus)))["pollen.grain.proportion",c("Estimate","Pr...t..")]), sim.r2.genus[2,6])
sim.coefs_quant.species <- cbind((data.frame(coef(summary(sim.quant.species)))["pollen.grain.proportion",c("Estimate","Pr...t..")]), sim.r2.species[2,6])
#Rename row names and column names
row.names(sim.coefs_quant.family) = "Family"
row.names(sim.coefs_quant.genus) = "Genus"
row.names(sim.coefs_quant.species) = "Species"
colnames(sim.coefs_quant.family) = c("Slope", "p-value", "R2")
colnames(sim.coefs_quant.genus) = c("Slope", "p-value", "R2")
colnames(sim.coefs_quant.species) = c("Slope", "p-value", "R2")
#Merge summary of coefficients into one dataset
sim.coefs_summ <- rbind(sim.coefs_quant.family, sim.coefs_quant.genus, sim.coefs_quant.species)
#Display table of summarized coefficients
kable(sim.coefs_summ) %>%
kable_styling(bootstrap_options = "striped", full_width = F)| Slope | p-value | R2 | |
|---|---|---|---|
| Family | 0.6831743 | 0.0e+00 | 0.9916468 |
| Genus | 0.3066793 | 1.8e-06 | 0.7519940 |
| Species | 0.3066793 | 1.8e-06 | 0.7519940 |
Pollen grain proportion is significantly correlated with read proportion at the family (R2=0.99, p<0.000001), genus (R2=0.75, p=0.000002), and species level (R2=0.75, p=0.000002). However, these data are so sparse that this may not be meaningful.
4.5 comparison of empirical and simulation kraken data
To compare the empirical and simulation kraken data, use a binomial mixed effects model to look at the counts of false positives vs. true positives, with repID nested in sample nested in mix.ID as a random effect.
taxon = c("species", "genus", "family")
depth = c("1GB", "2GB", "all")
# first set up a table for the results:
# I will set this up with 0 entries
krak_sim.results.table = data.frame(taxon = rep(NA,9), depth = rep(NA,9), p.val = rep(1.000001,9), n = rep(9999,9), warning.msg = rep(NA,9))
# keep track of which row of the table to record in:
tracker = 1
# # EXAMPLE FORMULA
# glmer(cbind(true_pos,false_pos) ~ source + (1|mix.ID/sample/rep.ID), family = binomial, data = krakamp_its_species, control = glmerControl(optimizer="bobyqa"))
for(k in 1:3) { # 'taxon': species, genus, family
for(l in 1:3){ # 'depth': 1GB, 2GB, or all
# first, name the analysis:
namer = paste(taxon[k], ".", depth[l], sep = "")
# second, set which taxonomic data to use:
data.to.use = paste("krak_simkrak_agg_", taxon[k], "_", depth[l], sep = "")
# third, set up mixed-effects model:
mixed = paste(namer, " = suppressWarnings(glmer(cbind(true_pos,false_pos) ~ source + (1|mix.ID/sample/rep.ID), family = binomial, data =", data.to.use, ", control = glmerControl(optimizer=\"bobyqa\")))", sep = "")
# fourth, evaluate the mixed-effects model
eval(parse(text = mixed))
## extract p-value
pvaller = paste("pval <- coef(summary(", namer, "))[2,4]", sep = "")
eval(parse(text = pvaller))
# extract convergence failures
converger = paste(namer, "@optinfo$conv$lme4$code", sep = "")
converg = eval(parse(text = converger))
converg.return = ifelse(length(converg)==1, "ERROR!!", "")
# record results in table
krak_sim.results.table[tracker,1] = taxon[k]
krak_sim.results.table[tracker,2] = depth[l]
krak_sim.results.table[tracker,3] = pval
krak_sim.results.table[tracker,4] = nrow(eval(parse(text = data.to.use)))
krak_sim.results.table[tracker,5] = converg.return
# advance tracker
tracker = tracker + 1
}
}
# display results table
# note that the 'kable' function is part of the 'knitr' package, which I required at the very top of this document.
# in this case, not displaying the table (commented out) since all but one row gave an error.
# kable(krak_sim.results.table)The results table for the GLMMs is not displayed, because all but one of the models had errors. This is probably due to the very small sample size for the simulated data.
The one model that worked was at the family level, with 1GB depth, where the difference between simulated and empirical datasets was highly significant; from the tables below one can see that the simulated data have fewer false-positive matches at the family level relative to the empirical data.
4.6 comparison of amplicon and shotgun results: Quantitative data - AIC comparison
This analysis uses AIC to determine which dataset (amplicon vs. WGS) provides a better fit when modelling pollen grain proportion vs. read proportion. The AIC for these models will give some idea as to which dataset is more quantiatively accurate.
#Import quantitative results from amplicon analysis
load("quant_amplicon_results.RData")
#Create data frame to hold ITS results: 3 taxon levels x 1 marker = 3 rows
ITS_comp_AIC= data.frame(taxon = rep(NA,3), Krak_AIC = rep(NA,3), Amp_AIC = rep(NA,3))
rbcL_comp_AIC= data.frame(taxon = rep(NA,3), Krak_AIC = rep(NA,3), Amp_AIC = rep(NA,3))
its_tracker = 1
rbc_tracker = 1
#Loop for ITS table
for(k in 1:3) { # 'taxon': species, genus, family
# first, name the comparison:
namer = taxon[k]
# second, set which taxonomic data to use:
krak_data.to.use = paste("quant.", taxon[k], sep = "")
amp_data.to.use = paste("quant.ITS.", taxon[k], sep = "")
## extract Kraken AIC
# example: extractAIC(Krak.Q1.genus.all)
Krak_AICer = paste("krakaic <- extractAIC(",krak_data.to.use,")[2]", sep = "")
eval(parse(text = Krak_AICer))
## extract Amplicon AIC
# example: extractAIC(Q1.genus.ITS.all)
Amp_AICer = paste("ampaic <- extractAIC(",amp_data.to.use,")[2]", sep = "")
eval(parse(text = Amp_AICer))
# record results in table
ITS_comp_AIC[its_tracker,1] = taxon[k]
ITS_comp_AIC[its_tracker,2] = krakaic
ITS_comp_AIC[its_tracker,3] = ampaic
# advance tracker
its_tracker = its_tracker + 1
}4.6.1 ITS2 results quantitative AIC:
kable(ITS_comp_AIC) %>%
kable_styling(bootstrap_options = "striped", full_width = F)| taxon | Krak_AIC | Amp_AIC |
|---|---|---|
| species | -1123.688 | -158.85885 |
| genus | -1050.536 | -46.83037 |
| family | -1020.748 | -38.44006 |
#Repeat for rbcL
for(k in 1:3) { # 'taxon': species, genus, family
# first, name the comparison:
namer = taxon[k]
# second, set which taxonomic data to use:
krak_data.to.use = paste("quant.", taxon[k], sep = "")
amp_data.to.use = paste("quant.rbcL.", taxon[k], sep = "")
## extract Kraken AIC
# example: extractAIC(Krak.Q1.genus.all)
Krak_AICer = paste("krakaic <- extractAIC(",krak_data.to.use,")[2]", sep = "")
eval(parse(text = Krak_AICer))
## extract Amplicon AIC
# example: extractAIC(Q1.genus.ITS.all)
Amp_AICer = paste("ampaic <- extractAIC(",amp_data.to.use,")[2]", sep = "")
eval(parse(text = Amp_AICer))
# record results in table
rbcL_comp_AIC[rbc_tracker,1] = taxon[k]
rbcL_comp_AIC[rbc_tracker,2] = krakaic
rbcL_comp_AIC[rbc_tracker,3] = ampaic
# advance tracker
rbc_tracker = rbc_tracker + 1
}4.6.2 rbcL results quantitative AIC:
kable(rbcL_comp_AIC) %>%
kable_styling(bootstrap_options = "striped", full_width = F)| taxon | Krak_AIC | Amp_AIC |
|---|---|---|
| species | -1123.688 | -211.3039 |
| genus | -1050.536 | -181.7730 |
| family | -1020.748 | -124.5663 |
For all taxonomic levels, the AICs for the Kraken data are much smaller than for the amplicon data. This implies that the read proportion of the Kraken data is more quantitatively predictive of true pollen grain proportion.
4.7 Amplicon vs. Shotgun comparison - GLMM of true positives and false positives
This analysis uses binomial mixed models to assess if using amplicon vs. WGS has a significant effect on the ability to qualitatively detect true positives vs. false positives. Datasets were aggregated into counts of true positives vs. false positives by mixID, as was done for the comparison of empirical vs. simulated kraken data.
4.7.1 by marker and by taxonomic level (Amplicon vs Shotgun GLMM)
First we will split out the analysis by amplicon marker (rbcL vs. ITS2) and by taxonomic level of identification (family / genus / species); next we will combine both amplicons but continue to assess at all three taxonomic levels.
taxon = c("species", "genus", "family")
marker = c("its", "rbc")
# set up a data frame for the results:
# I will set this up with 6 rows (3 taxonomy levels x 2 markers)
krakamp.results.table = data.frame(taxon = rep(NA,6), marker = rep(NA,6), p.val = rep(1.000001,6), n = rep(9999,6), warning.msg = rep(NA,6))
# keep track of which row of the table to record in:
tracker = 1
# # EXAMPLE FORMULA
# glmer(cbind(true_pos,false_pos) ~ source + (1|mix.ID/sample/rep.ID), family = binomial, data = krakamp_its_species, control = glmerControl(optimizer="bobyqa"))
for(k in 1:3) { # 'taxon': species, genus, family
for(l in 1:2){ # 'marker': ITS2 or rbcL
# first, name the analysis:
namer = paste(taxon[k], ".", marker[l], sep = "")
# second, set which taxonomic data to use:
data.to.use = paste("krakamp_", marker[l], "_", taxon[k], sep = "")
# third, set up mixed-effects model:
mixed = paste(namer, " = suppressWarnings(glmer(cbind(true_pos,false_pos) ~ source + (1|mix.ID/sample/rep.ID), family = binomial, data =", data.to.use, ", control = glmerControl(optimizer=\"bobyqa\")))", sep = "")
# fourth, evaluate the mixed-effects model
eval(parse(text = mixed))
## extract p-value
pvaller = paste("pval <- coef(summary(", namer, "))[2,4]", sep = "")
eval(parse(text = pvaller))
# extract convergence failures
converger = paste(namer, "@optinfo$conv$lme4$code", sep = "")
converg = eval(parse(text = converger))
converg.return = ifelse(length(converg)==1, "ERROR!!", "")
# record results in table
krakamp.results.table[tracker,1] = taxon[k]
krakamp.results.table[tracker,2] = marker[l]
krakamp.results.table[tracker,3] = pval
krakamp.results.table[tracker,4] = nrow(eval(parse(text = data.to.use)))
krakamp.results.table[tracker,5] = converg.return
# advance tracker
tracker = tracker + 1
}
}
# display results table
# note that the 'kable' function is part of the 'knitr' package, which I required at the very top of this document.
kable(krakamp.results.table) %>%
kable_styling(bootstrap_options = "striped", full_width = F)| taxon | marker | p.val | n | warning.msg |
|---|---|---|---|---|
| species | its | 5.27e-04 | 84 | |
| species | rbc | 1.00e-07 | 84 | ERROR!! |
| genus | its | 0.00e+00 | 84 | ERROR!! |
| genus | rbc | 7.00e-07 | 84 | ERROR!! |
| family | its | 0.00e+00 | 84 | |
| family | rbc | 0.00e+00 | 84 | ERROR!! |
Most of the models gave errors, but \(p\)-values were highly significant for ITS2 at both the species and family levels (the only two results for which there were not errors). For each comparison, the amplicon data had a significantly lower rate of false positives/higher rate of true positives compared to the WGS data, corroborating the AIC analysis above.
4.7.2 markers combined (Amplicon vs Shotgun GLMM)
for this analysis we need to format the data a little differently; the formatted amplicon data are in different data frames based on the marker (ITS2 vs. rbcL). Need to combine those two to generate a single amplicon data frame; this has to happen at each taxonomic level. While seemingly easy with rbind, we have to take the additional step of aggregating for each mix.id and sample.id the two amplicon markers (add the false positives and false negatives together). Still, this is very straightforward with dplyr.
# first combine the two amplicon datasets, aggregating counts for the same mix and sample IDs
# this has to happen at each taxonomic level
ampkrak.family = rbind(amp_its_family_reshape, amp_rbc_family_reshape) %>% group_by(mix.ID, sample, rep.ID, source) %>% summarize(false_pos = sum(false_pos), true_pos = sum(true_pos))
ampkrak.genus = rbind(amp_its_genus_reshape, amp_rbc_genus_reshape) %>% group_by(mix.ID, sample, rep.ID, source) %>% summarize(false_pos = sum(false_pos), true_pos = sum(true_pos))
ampkrak.species = rbind(amp_its_species_reshape, amp_rbc_species_reshape) %>% group_by(mix.ID, sample, rep.ID, source) %>% summarize(false_pos = sum(false_pos), true_pos = sum(true_pos))
# then combine amplicon data with kraken data
ampkrak.family = rbind(data.frame(ampkrak.family), data.frame(agg.krak.family))
ampkrak.genus = rbind(data.frame(ampkrak.genus), data.frame(agg.krak.genus))
ampkrak.species = rbind(data.frame(ampkrak.species), data.frame(agg.krak.species))
# set up a data frame for the results with 3 rows (by taxonomic level)
krakamp.results.table = data.frame(taxon = rep(NA,3), p.val = rep(1.000001,3), n = rep(9999,3), warning.msg = rep(NA,3))
# keep track of which row of the table to record in:
tracker = 1
# # EXAMPLE FORMULA
# glmer(cbind(true_pos,false_pos) ~ source + (1|mix.ID/sample/rep.ID), family = binomial, data = krakamp_its_species, control = glmerControl(optimizer="bobyqa"))
for(k in 1:3) { # 'taxon': species, genus, family
# first, name the analysis:
namer = paste("krakamp.GLMM", taxon[k], sep = ".")
# second, set which taxonomic data to use:
data.to.use = paste("ampkrak", taxon[k], sep = ".")
# third, set up mixed-effects model:
mixed = paste(namer, " = suppressWarnings(glmer(cbind(true_pos,false_pos) ~ source + (1|mix.ID/sample/rep.ID), family = binomial, data = ", data.to.use, ", control = glmerControl(optimizer=\"bobyqa\")))", sep = "")
# fourth, evaluate the mixed-effects model
eval(parse(text = mixed))
## extract p-value
pvaller = paste("pval <- coef(summary(", namer, "))[2,4]", sep = "")
eval(parse(text = pvaller))
# extract convergence failures
converger = paste(namer, "@optinfo$conv$lme4$code", sep = "")
converg = eval(parse(text = converger))
converg.return = ifelse(length(converg)==1, "ERROR!!", "")
# record results in table
krakamp.results.table[tracker,1] = taxon[k]
# krakamp.results.table[tracker,2] = marker[l]
krakamp.results.table[tracker,2] = pval
krakamp.results.table[tracker,3] = nrow(eval(parse(text = data.to.use)))
krakamp.results.table[tracker,4] = converg.return
# advance tracker
tracker = tracker + 1
}
# display results table
# note that the 'kable' function is part of the 'knitr' package, which I required at the very top of this document.
kable(krakamp.results.table) %>%
kable_styling(bootstrap_options = "striped", full_width = F)| taxon | p.val | n | warning.msg |
|---|---|---|---|
| species | 0 | 137 | |
| genus | 0 | 137 | |
| family | 0 | 137 |
In contrast to the previous analysis (in which results were split out by marker), here—with markers combined—we had better model performance / convergence. For all three taxonomic levels, we see statistically significant differences between the amplicon and the shotgun data. All three of the coefficients are negative (as we move from amplicon to kraken), indicating that the “successes” (true positives) were reduced as we moved in that direction, i.e. that amplicon performance was better than kraken performance.
4.8 Amplicon vs. Shotgun comparison - GLMM of true positives and false positives
This analysis uses binomial mixed models to assess if using amplicon vs. WGS has a significant effect on the ability to qualitatively detect true positives vs. false negatives. This analysis is similar the qualitative mixed models to assess the effect of species richness, pollen grain proportion, etc. on the ability to qualitatively detect the correct taxa, but instead using “source” as the fixed effect.
4.8.1 by marker and taxonomic level
taxon = c("species", "genus", "family")
marker = c("its", "rbc")
# first set up a table for the results:
# I will set this up with 18 entries
krakamp.fn.results.table = data.frame(taxon = rep(NA,6), marker = rep(NA,6), model.name = rep(NA,6), p.val = rep(1.000001,6), n = rep(9999,6), warning.msg = rep(NA,6))
# keep track of which row of the table to record in:
tracker = 1
# # EXAMPLE FORMULA
# its.family = glmer(qual.family ~ source + (1|mix.ID/sample) + (1|family), family = binomial, data = krakamp_its_family_fn, control = glmerControl(optimizer="bobyqa"))
# response variables relating to each of the three questions (column names in data)
for(q in 1:2) { # 'marker': its or rbc
for(k in 1:3){ # 'taxon': species, genus, family
# first, name the analysis:
namer = paste(marker[q], ".", taxon[k], sep = "")
# # 1.5, print name of which model was evaluated
# print("**************************************************")
# print(paste("********************* ", namer, " *********************"))
# second, set which taxonomic data to use:
data.to.use = paste("krakamp_", marker[q], "_", taxon[k],"_fn", sep = "")
# third, set up mixed-effects model:
mixed = paste(namer, " = suppressWarnings(glmer(qual.", taxon[k],
" ~ source + (1|mix.ID/sample) + (1|", taxon[k], "), family = binomial,
data = ", data.to.use, ", control = glmerControl(optimizer=\"bobyqa\")))", sep = "")
# fourth, evaluate the mixed-effects model
eval(parse(text = mixed))
#fifth, print summary of model [SKIP FOR NOW]
#summarizer = paste("print(summary(", namer, "))", sep = "")
#eval(parse(text = summarizer)) # print summary of the mixed-effects model
## extract p-value
# example: coef(summary(its.family))[2,4]
pvaller = paste("pval <- coef(summary(", namer, "))[2,4]", sep = "")
eval(parse(text = pvaller))
# extract convergence failures
converger = paste(namer, "@optinfo$conv$lme4$code", sep = "")
converg = eval(parse(text = converger))
converg.return = ifelse(length(converg)==1, "ERROR!!", "")
# record results in table
krakamp.fn.results.table[tracker,1] = taxon[k]
krakamp.fn.results.table[tracker,2] = marker[q]
krakamp.fn.results.table[tracker,3] = namer
krakamp.fn.results.table[tracker,4] = pval
krakamp.fn.results.table[tracker,5] = nrow(eval(parse(text = data.to.use)))
krakamp.fn.results.table[tracker,6] = converg.return
# advance tracker
tracker = tracker + 1
}
}
# display results table
# note that the 'kable' function is part of the 'knitr' package and `kable_styling` is from the `kableExtra` package
kable(krakamp.fn.results.table) %>%
kable_styling(bootstrap_options = "striped", full_width = F)| taxon | marker | model.name | p.val | n | warning.msg |
|---|---|---|---|---|---|
| species | its | its.species | 0.4661678 | 461 | |
| genus | its | its.genus | 0.0084596 | 467 | |
| family | its | its.family | 0.7358638 | 509 | |
| species | rbc | rbc.species | 0.0004913 | 461 | |
| genus | rbc | rbc.genus | 0.0017134 | 467 | |
| family | rbc | rbc.family | 0.0000000 | 509 |
When ITS was used as a marker in the amplicon data, the amplicon data did not behave significantly better or worse than the WGS data at the species and family level. However, at the genus level (estimate = -1.15), the negative estimate and low p-value suggests that using ITS sequencing was more accurate for detecting true positives than WGS.
In contrast, when rbcL was used as a marker in the amplicon data, the amplicon data detected fewer true positives at the species (estimate = 1.19) and genus (estimate = 1.04) levels than the WGS data. However, the amplicon data detected more true positives at the family level (estimate = -26.774) than the WGS data.
4.8.2 by taxonomic level, with markers combined
This is a repeat of the analysis above, but with ITS and rbcL combined
#Create new qualitative variable in amplicon dataset that equals 1 if ITS and/or rbcL equals 1, 0 if else
amp_all$qual.family = ifelse(amp_all$qual.family.ITS == 1 | amp_all$qual.family.rbcL == 1, 1, 0)
amp_all$qual.genus = ifelse(amp_all$qual.genus.ITS == 1 | amp_all$qual.genus.rbcL == 1, 1, 0)
amp_all$qual.species = ifelse(amp_all$qual.species.ITS == 1 | amp_all$qual.species.rbcL == 1, 1, 0)
#Combine kraken and amplicon data
krakamp_family_fn <- rbind(truepos.krak.family[,c("mix.ID", "family", "sample", "qual.family", "source")], amp_all[,c("mix.ID","family","sample", "qual.family", "source")])
krakamp_genus_fn <- rbind(truepos.krak.genus[,c("mix.ID", "genus", "sample", "qual.genus", "source")], amp_all[,c("mix.ID","genus","sample", "qual.genus", "source")])
krakamp_species_fn <- rbind(truepos.krak.species[,c("mix.ID", "species", "sample", "qual.species", "source")], amp_all[,c("mix.ID","species","sample", "qual.species", "source")])
taxon = c("species", "genus", "family")
# first set up a table for the results:
# I will set this up with 18 entries
krakamp.fn.results.combined = data.frame(taxon = rep(NA,3), model.name = rep(NA,3), p.val = rep(1.000001,3), n = rep(9999,3), warning.msg = rep(NA,3))
# keep track of which row of the table to record in:
tracker = 1
# # EXAMPLE FORMULA
# its.family = glmer(qual.family ~ source + (1|mix.ID/sample) + (1|family), family = binomial, data = krakamp_family_fn, control = glmerControl(optimizer="bobyqa"))
# response variables relating to each of the three questions (column names in data)
for(k in 1:3){ # 'taxon': species, genus, family
# first, name the analysis:
namer = taxon[k]
# # 1.5, print name of which model was evaluated
# print("**************************************************")
# print(paste("********************* ", namer, " *********************"))
# second, set which taxonomic data to use:
data.to.use = paste("krakamp_", taxon[k],"_fn", sep = "")
# third, set up mixed-effects model:
mixed = paste(namer, " = suppressWarnings(glmer(qual.", taxon[k],
" ~ source + (1|mix.ID/sample) + (1|", taxon[k], "), family = binomial,
data = ", data.to.use, ", control = glmerControl(optimizer=\"bobyqa\")))", sep = "")
# fourth, evaluate the mixed-effects model
eval(parse(text = mixed))
#fifth, print summary of model [SKIP FOR NOW]
#summarizer = paste("print(summary(", namer, "))", sep = "")
#eval(parse(text = summarizer)) # print summary of the mixed-effects model
## extract p-value
# example: coef(summary(its.family))[2,4]
pvaller = paste("pval <- coef(summary(", namer, "))[2,4]", sep = "")
eval(parse(text = pvaller))
# extract convergence failures
converger = paste(namer, "@optinfo$conv$lme4$code", sep = "")
converg = eval(parse(text = converger))
converg.return = ifelse(length(converg)==1, "ERROR!!", "")
# record results in table
krakamp.fn.results.combined[tracker,1] = taxon[k]
krakamp.fn.results.combined[tracker,2] = namer
krakamp.fn.results.combined[tracker,3] = pval
krakamp.fn.results.combined[tracker,4] = nrow(eval(parse(text = data.to.use)))
krakamp.fn.results.combined[tracker,5] = converg.return
# advance tracker
tracker = tracker + 1
}
# display results table
# note that the 'kable' function is part of the 'knitr' package and `kable_styling` is from the `kableExtra` package
kable(krakamp.fn.results.combined) %>%
kable_styling(bootstrap_options = "striped", full_width = F)| taxon | model.name | p.val | n | warning.msg |
|---|---|---|---|---|
| species | species | 0.0339795 | 417 | |
| genus | genus | 0.0000720 | 423 | |
| family | family | 0.0000000 | 465 |
After combining rbcL and ITS2, where taxa were determined as present if they were present in rbcL OR ITS2, amplicon performed better than kraken at the species level (estimate = -0.948, p=0.034), genus level (estimate = -30.28, p<0.00001), and family level (estimate = -68.21, p<0.00001).
5 figures
5.1 figure 1
This figure shows the mean proportion and binomial CI of correct taxonomic matches by species and genus. Fig. 1a is by family, Fig. 1b is by genus, and Fig. 1c is by species.
The binomial CI is calculated using the binom.confint function, which requires a vector of the number of success (x) and a vector of the number of independent trials (n).
#Need to detach plyr for code to work
detach(package:plyr)## Error in detach(package:plyr): invalid 'name' argument#Create variable "presence" to get total number of independent tests
truepos.krak.family$presence = 1
truepos.krak.genus$presence = 1
truepos.krak.species$presence = 1
#Total the number of taxa correctly identified (x) when grouped by taxa
family_x = truepos.krak.family %>%
group_by(family) %>%
summarise(x = sum(qual.family))
genus_x = truepos.krak.genus %>%
group_by(genus) %>%
summarise(x = sum(qual.genus))
species_x = truepos.krak.species %>%
group_by(species) %>%
summarise(x = sum(qual.species))
#Get the total number of independent tests (n) when grouped by taxa
family_n = truepos.krak.family %>%
group_by(family) %>%
summarise(n = sum(presence))
genus_n = truepos.krak.genus %>%
group_by(genus) %>%
summarise(n = sum(presence))
species_n = truepos.krak.species %>%
group_by(species) %>%
summarise(n = sum(presence))
#Calculate binomial confidence interval using x and n
binomci_family = binom.confint(family_x$x, family_n$n, method="exact")
binomci_species = binom.confint(species_x$x, species_n$n, method="exact")
binomci_genus = binom.confint(genus_x$x, genus_n$n, method="exact")
#Add the taxa names, rename column
binomci_family = cbind(family_x$family, binomci_family)
binomci_genus = cbind(genus_x$genus, binomci_genus)
binomci_species = cbind(species_x$species, binomci_species)
colnames(binomci_family)[1] = "family"
colnames(binomci_genus)[1] = "genus"
colnames(binomci_species)[1] = "species"
#Figure 1A: Family level
fig1a=binomci_family%>%
ggplot(aes(family,mean))+
geom_point(position=position_dodge(width=0.3), size=4, alpha=0.6)+
geom_errorbar(width=0.6, aes(family,ymin=lower, ymax=upper), alpha=0.6, position=position_dodge(width=0.3))+
xlab("family")+
ylab("proportion of correct matches")+
theme_bw()+
ggtitle("A")+
theme(axis.text.x=element_text(angle=45,hjust=1))
#Figure 1B: Genus level
fig1b=binomci_genus%>%
ggplot(aes(genus,mean))+
geom_point(position=position_dodge(width=0.3), size=4, alpha=0.6)+
geom_errorbar(width=0.6, aes(genus,ymin=lower, ymax=upper), alpha=0.6, position=position_dodge(width=0.3))+
xlab("genus")+
ylab("proportion of correct matches")+
theme_bw()+
ggtitle("B")+
theme(axis.text.x=element_text(angle=45,hjust=1))
#Figure 1C: Species level
fig1c=binomci_species%>%
ggplot(aes(species,mean))+
geom_point(position=position_dodge(width=0.3), size=4, alpha=0.6)+
geom_errorbar(width=0.6, aes(species,ymin=lower, ymax=upper), alpha=0.6, position=position_dodge(width=0.3))+
xlab("species")+
ylab("proportion of correct matches")+
theme_bw()+
ggtitle("C")+
theme(axis.text.x=element_text(angle=45,hjust=1))
#Figure 1 panel
ggsave("WGS_fig1a.pdf", plot=fig1a, device="pdf", height=5, width=7, units="in")
ggsave("WGS_fig1b.pdf", plot=fig1b, device="pdf", height=5, width = 7, units="in")
ggsave("WGS_fig1c.pdf", plot=fig1c, device="pdf", height=5, width = 7, units="in")
panel.fig1 = grid.arrange(fig1a, fig1b,fig1c, ncol=1)ggsave("fig1_combined.jpg", plot=panel.fig1, device="jpeg", height=11, width = 8.5, units="in")
Figure 1
5.2 figure 2
This figure shows the proportion of correct taxonomic matchess by species richness for rbcL and ITS2 at the species and genus level. To correctly format the data, the mean and binomial CI of correct taxonomic matches needs to be summarized 3 times: once for sample, once for mix, and once for level of species richness. Otherwise samples with multiple species will get over-represented in terms of the overall means. The integers are tracked at each step and summarized with dplyr. The data is summarized with dplyr twice, once per mix and then once per sample. Then within each level of the factor of interest, the mean and binomial confidence intervals are calculated using binom.confint.
#Summarize the agg dataset first by species richness, mix.ID, and sample. Also calculate pool size for each sample ('n()'), as well as sum # of positive identifications
CI.family.1 = truepos.krak.family %>%
select(mix.ID, sample, spp.rich, qual.family, question.1, question.2, question.3) %>% group_by(spp.rich, mix.ID, sample) %>%
summarize(pool.size = n(),
family = sum(qual.family),
#add question.1, question.2, question.3 because those equal to 1 are filtered out in Jamie's figure 3 code
question.1 = mean(question.1),
question.2 = mean(question.2),
question.3 = mean(question.3))
CI.genus.1 = truepos.krak.genus %>%
select(mix.ID, sample, spp.rich, qual.genus, question.1, question.2, question.3) %>% group_by(spp.rich, mix.ID, sample) %>%
summarize(pool.size = n(),
genus = sum(qual.genus),
#add question.1, question.2, question.3 because those equal to 1 are filtered out in Jamie's figure 3 code
question.1 = mean(question.1),
question.2 = mean(question.2),
question.3 = mean(question.3))
CI.species.1 = truepos.krak.species %>%
select(mix.ID, sample, spp.rich, qual.species, question.1, question.2, question.3) %>% group_by(spp.rich, mix.ID, sample) %>%
summarize(pool.size = n(),
species = sum(qual.species),
#add question.1, question.2, question.3 because those equal to 1 are filtered out in Jamie's figure 3 code
question.1 = mean(question.1),
question.2 = mean(question.2),
question.3 = mean(question.3))
# second step: group_by(spp.rich, mix.ID, pool.size)
CI.family.2 = CI.family.1 %>% group_by(spp.rich, mix.ID, pool.size) %>%
summarize(pool.num = n(),
family = sum(family),
question.1 = mean(question.1),
question.2 = mean(question.2),
question.3 = mean(question.3)) # %>%
# multiply pool size & number to get total number of possibilities of matches
CI.family.2$n2 = CI.family.2$pool.size * CI.family.2$pool.num
CI.genus.2 = CI.genus.1 %>% group_by(spp.rich, mix.ID, pool.size) %>%
summarize(pool.num = n(),
genus = sum(genus),
question.1 = mean(question.1),
question.2 = mean(question.2),
question.3 = mean(question.3)) # %>%
# multiply pool size & number to get total number of possibilities of matches
CI.genus.2$n2 = CI.genus.2$pool.size * CI.genus.2$pool.num
CI.species.2 = CI.species.1 %>% group_by(spp.rich, mix.ID, pool.size) %>%
summarize(pool.num = n(),
species = sum(species),
question.1 = mean(question.1),
question.2 = mean(question.2),
question.3 = mean(question.3)) # %>%
# multiply pool size & number to get total number of possibilities of matches
CI.species.2$n2 = CI.species.2$pool.size * CI.species.2$pool.num
#Format variables to be factors. Restrict to mixtures where questions are equal to 1. **Not sure why this is done?
dat.family <- CI.family.2
dat.family$question.1=as.factor(dat.family$question.1)
dat.family$question.2=as.factor(dat.family$question.2)
dat.family$question.3=as.factor(dat.family$question.3)
dat.family$spp.rich=as.factor(dat.family$spp.rich)
#dat.family=filter(dat.family,question.1=="1"|question.2=="1"|question.3=="1")
fig2_family=dat.family%>%
select(spp.rich, mix.ID, n2, family)
dat.genus <- CI.genus.2
dat.genus$question.1=as.factor(dat.genus$question.1)
dat.genus$question.2=as.factor(dat.genus$question.2)
dat.genus$question.3=as.factor(dat.genus$question.3)
dat.genus$spp.rich=as.factor(dat.genus$spp.rich)
#dat.genus=filter(dat.genus,question.1=="1"|question.2=="1"|question.3=="1")
fig2_genus=dat.genus%>%
select(spp.rich, mix.ID, n2, genus)
dat.species <- CI.species.2
dat.species$question.1=as.factor(dat.species$question.1)
dat.species$question.2=as.factor(dat.species$question.2)
dat.species$question.3=as.factor(dat.species$question.3)
dat.species$spp.rich=as.factor(dat.species$spp.rich)
#dat.species=filter(dat.species,question.1=="1"|question.2=="1"|question.3=="1")
fig2_species=dat.species%>%
select(spp.rich, mix.ID, n2, species)
#Convert from wide to long
fig2_long_family = melt(fig2_family, id.vars = c("spp.rich","mix.ID", "n2"))
fig2_long_genus = melt(fig2_genus, id.vars = c("spp.rich","mix.ID", "n2"))
fig2_long_species = melt(fig2_species, id.vars = c("spp.rich","mix.ID", "n2"))
#Subset to species richness
spp.rich_family = fig2_long_family%>%
filter(substr(variable,1,7)=="family")%>%
rename(x=value, n=n2)
spp.rich_genus = fig2_long_genus%>%
filter(substr(variable,1,7)=="genus")%>%
rename(x=value, n=n2)
spp.rich_species = fig2_long_species%>%
filter(substr(variable,1,7)=="species")%>%
rename(x=value, n=n2)
#group by species richness and marker so that all mixtures with richness = 2 are grouped together
spp.rich_family = spp.rich_family %>%
group_by(spp.rich, variable) %>%
summarize(n=mean(n),x=mean(x)) %>%
ungroup()
spp.rich_genus = spp.rich_genus %>%
group_by(spp.rich, variable) %>%
summarize(n=mean(n),x=mean(x)) %>%
ungroup()
spp.rich_species = spp.rich_species %>%
group_by(spp.rich, variable) %>%
summarize(n=mean(n),x=mean(x)) %>%
ungroup()
#Calculate mean and binomial CI
bin.family = binom.confint(spp.rich_family$x, spp.rich_family$n,methods="exact") %>%
select(-method)
bin.genus = binom.confint(spp.rich_genus$x, spp.rich_genus$n,methods="exact") %>%
select(-method)
bin.species = binom.confint(spp.rich_species$x, spp.rich_species$n,methods="exact") %>%
select(-method)
#Merge mean and binomial CI with mixture info
family.all = merge(bin.family, spp.rich_family, by = c("x", "n"))
family.all = unique(family.all)
genus.all = merge(bin.genus, spp.rich_genus, by = c("x", "n"))
genus.all = unique(genus.all)
species.all = merge(bin.species, spp.rich_species, by = c("x", "n"))
species.all = unique(species.all)
#Figure 2A: Family level
fig2a=family.all%>%
filter(variable=="family")%>%
ggplot()+
geom_point(aes(spp.rich,mean))+
geom_errorbar(aes(spp.rich,ymin=lower, ymax=upper), width=0.2)+
xlab("species richness")+
ylab("proportion of correct matches")+
ylim(0,1)+
ggtitle("A")+
theme_bw()
#Figure 2B: Genus level
fig2b=genus.all%>%
filter(variable=="genus")%>%
ggplot()+
geom_point(aes(spp.rich,mean))+
geom_errorbar(aes(spp.rich,ymin=lower, ymax=upper), width=0.2)+
xlab("species richness")+
ylab("proportion of correct matches")+
ylim(0,1)+
ggtitle("B")+
theme_bw()
#Figure 2A: Species level
fig2c=species.all%>%
filter(variable=="species")%>%
ggplot()+
geom_point(aes(spp.rich,mean))+
geom_errorbar(aes(spp.rich,ymin=lower, ymax=upper), width=0.2)+
xlab("species richness")+
ylab("proportion of correct matches")+
ylim(0,1)+
ggtitle("C")+
theme_bw()
#Create panel for Figure 2
fig2=grid.arrange(fig2a,fig2b,fig2c, nrow=1)ggsave("fig2_combined.jpg", plot=fig2, device="jpeg", width=169, units="mm")## Saving 169 x 127 mm image
Figure 2
5.3 Figure 3
This figure shows the relationship between the proportion of pollen grains belonging to a particular taxa with the probability of detection (presence/absence) of that taxa in the sequencing reads. Each color belongs to a particular taxa, and each taxa has its own trendline as determined by logistic regression.
fig3_sub_family=truepos.krak.family%>%
select(mix.ID, family, spp.rich, pollen.grain.proportion, qual.family)
fig3_sub_family$spp.rich=as.factor(fig3_sub_family$spp.rich)
fig3_sub_genus=truepos.krak.genus%>%
select(mix.ID, genus, spp.rich, pollen.grain.proportion, qual.genus)
fig3_sub_genus$spp.rich=as.factor(fig3_sub_genus$spp.rich)
fig3_sub_species=truepos.krak.species%>%
select(mix.ID, species, spp.rich, pollen.grain.proportion, qual.species)
fig3_sub_genus$spp.rich=as.factor(fig3_sub_genus$spp.rich)
#Figure 3a: Family level
fig3a=fig3_sub_family%>%
group_by(family)%>%
ggplot(aes(pollen.grain.proportion, qual.family, color = family, shape = family))+
geom_jitter( size=4, alpha=0.4, height=0.05 )+
geom_smooth(method = "glm", method.args = list(family = "binomial"), se = FALSE, alpha=0.2) +
ggtitle("A")+
xlab("proportion of pollen grains in the sample")+
ylab("probability of detection")+
theme_bw()+
theme(legend.key = element_rect(size = 5),
legend.key.size = unit(1.5, 'lines'))
#Figure 3b: Genus level
fig3b=fig3_sub_genus%>%
group_by(genus)%>%
ggplot(aes(pollen.grain.proportion, qual.genus, color = genus, shape = genus))+
geom_jitter( size=4, alpha=0.4, height=0.05 )+
geom_smooth(method = "glm", method.args = list(family = "binomial"), se = FALSE, alpha=0.2) +
ggtitle("B")+
xlab("proportion of pollen grains in the sample")+
ylab("probability of detection")+
theme_bw()+
theme(legend.key = element_rect(size = 5),
legend.key.size = unit(1.5, 'lines'))
#Figure 3c: Species level
fig3c=fig3_sub_species%>%
group_by(species)%>%
ggplot(aes(pollen.grain.proportion, qual.species, color = species, shape = species))+
geom_jitter( size=4, alpha=0.4, height=0.05 )+
geom_smooth(method = "glm", method.args = list(family = "binomial"), se = FALSE, alpha=0.2) +
ggtitle("C")+
xlab("proportion of pollen grains in the sample")+
ylab("probability of detection")+
theme_bw()+
theme(legend.key = element_rect(size = 5),
legend.key.size = unit(1.5, 'lines'))
#Create panel for figure 3
fig3=grid.arrange(fig3a,fig3b,fig3c, nrow=1)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.## Warning: Removed 20 rows containing missing values (geom_point).## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 8.
## Consider specifying shapes manually if you must have them.## Warning: Removed 14 rows containing missing values (geom_point).ggsave("WGS_fig3a.pdf", plot=fig3a, device="pdf", width=7, height=5, units="in")## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredggsave("WGS_fig3b.pdf", plot=fig3b, device="pdf", width=7, height=5, units="in")## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.## Warning: Removed 20 rows containing missing values (geom_point).ggsave("WGS_fig3c.pdf", plot=fig3c, device="pdf", width=7, height=5, units="in")## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 8.
## Consider specifying shapes manually if you must have them.## Warning: Removed 14 rows containing missing values (geom_point).panel.fig3 = grid.arrange(fig3a, fig3b, fig3c, ncol=1) # heights = rep(50,3)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.## Warning: Removed 20 rows containing missing values (geom_point).## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 8.
## Consider specifying shapes manually if you must have them.## Warning: Removed 14 rows containing missing values (geom_point).ggsave("fig3_combined.jpg", plot=panel.fig3, device="jpeg", height=8.5, width = 11, units="in")
Figure 3
5.4 Figure 4
This figure shows the quantitative relationship between the proportion of pollen grains and the proportion of reads matching to a particular taxa. There are plots for the species, genus, and family level.
Fortunately, these figures do not require formatting of the dataset beyond what has already been done. The code is fairly self-explanatory.
#Figure 4A: Family level
fig4a=truepos.krak.family%>%
ggplot()+
geom_point(aes(pollen.grain.proportion, quant.family, color=family, shape=family), size=4, alpha=0.4, position = "jitter")+
xlab("proportion of pollen grains in sample")+
ylab("proportion of reads")+
geom_abline(size=0.2, alpha=1)+ #line w/ slope 1
theme_bw()+
ggtitle("A")+
scale_shape_manual(values=c(15,16,17,18,0,1,2,5,6))
#Figure 4B: Genus level
fig4b=truepos.krak.genus%>%
ggplot()+
geom_point(aes(pollen.grain.proportion, quant.genus, color=genus, shape=genus), size=4, alpha=0.4, position = "jitter")+
xlab("proportion of pollen grains in sample")+
ylab("proportion of reads")+
geom_abline(size=0.2, alpha=1)+ #line w/ slope 1
theme_bw()+
ggtitle("B")+
scale_shape_manual(values=c(15,16,17,18,0,1,2,5,6))
#Figure 4C: Species level
fig4c=truepos.krak.species%>%
ggplot()+
geom_point(aes(pollen.grain.proportion, quant.species, color=species, shape=species), size=4, alpha=0.4, position = "jitter")+
xlab("proportion of pollen grains in sample")+
ylab("proportion of reads")+
geom_abline(size=0.2, alpha=1)+ #line w/ slope 1
theme_bw()+
ggtitle("C")+
scale_shape_manual(values=c(15,16,17,18,0,1,2,5,6))
#Figure 4 Panel
ggsave("WGS_fig4a.pdf", fig4a, device="pdf", width=7, height=5, units="in")
ggsave("WGS_fig4b.pdf", fig4b, device="pdf", width=7, height=5, units="in")
ggsave("WGS_fig4c.pdf", fig4c, device="pdf", width=7, height=5, units="in")
# save panel
panel.fig4 = grid.arrange(fig4a, fig4b, fig4c,ncol=1) # heights = rep(50,3)ggsave("fig4_combined.jpg", plot=panel.fig4, device="jpeg", height=11, width = 11, units="in")
Figure 4
5.5 figure 5
This figure shows the proportion of correct taxonomic matchess by source (amplicon vs. WGS) at the species, genus, and family level. To correctly format the data, the mean and binomial CI of correct taxonomic matches needs to be summarized 3 times: once for sample, once for mix, and once for source. Otherwise samples with multiple species will get over-represented in terms of the overall means. The integers are tracked at each step and summarized with dplyr. The data is summarized with dplyr twice, once per mix and then once per sample. Then within each level of the factor of interest, the mean and binomial confidence intervals are calculated using binom.confint.
#Summarize the agg dataset first by source, mix.ID, and sample. Also calculate pool size for each sample ('n()'), as well as sum # of positive identifications
CI.krakamp.family.fn.1 = krakamp_family_fn %>%
select(mix.ID, sample, qual.family, source) %>% group_by(source, mix.ID, sample) %>%
summarize(pool.size = n(),
family = sum(as.numeric(qual.family)))
CI.krakamp.genus.fn.1 = krakamp_genus_fn %>%
select(mix.ID, sample, qual.genus, source) %>% group_by(source, mix.ID, sample) %>%
summarize(pool.size = n(),
genus = sum(as.numeric(qual.genus)))
CI.krakamp.species.fn.1 = krakamp_species_fn %>%
select(mix.ID, sample, qual.species, source) %>% group_by(source, mix.ID, sample) %>%
summarize(pool.size = n(),
species = sum(as.numeric(qual.species)))
# second step: group_by(source, mix.ID, pool.size)
CI.krakamp.family.fn.2 = CI.krakamp.family.fn.1 %>% group_by(source, mix.ID, pool.size) %>%
summarize(pool.num = n(),
family = sum(family))
# multiply pool size & number to get total number of possibilities of matches
CI.krakamp.family.fn.2$n2 = CI.krakamp.family.fn.2$pool.size * CI.krakamp.family.fn.2$pool.num
#format source to be factor
CI.krakamp.family.fn.2$source=as.factor(CI.krakamp.family.fn.2$source)
fig5_family=CI.krakamp.family.fn.2%>%
select(source, mix.ID, n2, family)
CI.krakamp.genus.fn.2 = CI.krakamp.genus.fn.1 %>% group_by(source, mix.ID, pool.size) %>%
summarize(pool.num = n(),
genus = sum(genus))
# multiply pool size & number to get total number of possibilities of matches
CI.krakamp.genus.fn.2$n2 = CI.krakamp.genus.fn.2$pool.size * CI.krakamp.genus.fn.2$pool.num
#format source to be factor
CI.krakamp.genus.fn.2$source=as.factor(CI.krakamp.genus.fn.2$source)
fig5_genus=CI.krakamp.genus.fn.2%>%
select(source, mix.ID, n2, genus)
CI.krakamp.species.fn.2 = CI.krakamp.species.fn.1 %>% group_by(source, mix.ID, pool.size) %>%
summarize(pool.num = n(),
species = sum(species))
# multiply pool size & number to get total number of possibilities of matches
CI.krakamp.species.fn.2$n2 = CI.krakamp.species.fn.2$pool.size * CI.krakamp.species.fn.2$pool.num
#format source to be factor
CI.krakamp.species.fn.2$source=as.factor(CI.krakamp.species.fn.2$source)
fig5_species=CI.krakamp.species.fn.2%>%
select(source, mix.ID, n2, species)
#Convert from wide to long
fig5_long_family = melt(fig5_family, id.vars = c("source","mix.ID", "n2"))
fig5_long_family = fig5_long_family%>%
rename(x=value, n=n2)
fig5_long_genus = melt(fig5_genus, id.vars = c("source","mix.ID", "n2"))
fig5_long_genus = fig5_long_genus%>%
rename(x=value, n=n2)
fig5_long_species = melt(fig5_species, id.vars = c("source","mix.ID", "n2"))
fig5_long_species = fig5_long_species%>%
rename(x=value, n=n2)
#group by source
fig5_long_family = fig5_long_family %>%
group_by(source) %>%
summarize(n=mean(n),x=mean(x)) %>%
ungroup()
fig5_long_genus = fig5_long_genus %>%
group_by(source) %>%
summarize(n=mean(n),x=mean(x)) %>%
ungroup()
fig5_long_species = fig5_long_species %>%
group_by(source) %>%
summarize(n=mean(n),x=mean(x)) %>%
ungroup()
#Calculate mean and binomial CI
fig5_bin.family = binom.confint(fig5_long_family$x, fig5_long_family$n,methods="exact") %>%
select(-method)
fig5_bin.genus = binom.confint(fig5_long_genus$x, fig5_long_genus$n,methods="exact") %>%
select(-method)
fig5_bin.species = binom.confint(fig5_long_species$x, fig5_long_species$n,methods="exact") %>%
select(-method)
#merge datasets
fig5_family.all = merge(fig5_bin.family, fig5_long_family, by = c("x", "n"))
fig5_family.all = unique(fig5_family.all)
fig5_genus.all = merge(fig5_bin.genus, fig5_long_genus, by = c("x", "n"))
fig5_genus.all = unique(fig5_genus.all)
fig5_species.all = merge(fig5_bin.species, fig5_long_species, by = c("x", "n"))
fig5_species.all = unique(fig5_species.all)
#Figure 5A: Family level
fig5a=fig5_family.all%>%
ggplot()+
geom_point(aes(source,mean))+
geom_errorbar(aes(source,ymin=lower, ymax=upper), width=0.2)+
xlab("source")+
ylab("proportion of correct matches")+
ylim(0,1)+
ggtitle("A")+
theme_bw()
#Figure 5B: Genus level
fig5b=fig5_genus.all%>%
ggplot()+
geom_point(aes(source,mean))+
geom_errorbar(aes(source,ymin=lower, ymax=upper), width=0.2)+
xlab("source")+
ylab("proportion of correct matches")+
ylim(0,1)+
ggtitle("B")+
theme_bw()
#Figure 5C: Species level
fig5c=fig5_species.all%>%
ggplot()+
geom_point(aes(source,mean))+
geom_errorbar(aes(source,ymin=lower, ymax=upper), width=0.2)+
xlab("source")+
ylab("proportion of correct matches")+
ylim(0,1)+
ggtitle("C")+
theme_bw()
#Create panel for Figure 5
fig5=grid.arrange(fig5a,fig5b,fig5c, nrow=1)ggsave("fig5_combined.jpg", plot=fig5, device="jpeg", width=169, units="mm")## Saving 169 x 127 mm image
Figure 5